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Journal ArticleDOI

Enabling Effective Programming and Flexible Management of Efficient Body Sensor Network Applications

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TLDR
This paper analyzes the most important requirements for an effective BSN-specific software framework, enabling efficient signal-processing applications and presents signal processing in node environment (SPINE), an open-source programming framework, designed to support rapid and flexible prototyping and management of BSN applications.
Abstract: 
Wireless body sensor networks (BSNs) possess enormous potential for changing people's daily lives. They can enhance many human-centered application domains such as m-Health, sport and wellness, and human-centered applications that involve physical/virtual social interactions. However, there are still challenging issues that limit their wide diffusion in real life: primarily, the programming complexity of these systems, due to the lack of high-level software abstractions, and the hardware constraints of wearable devices. In contrast with low-level programming and general-purpose middleware, domain-specific frameworks are an emerging programming paradigm designed to fulfill the lack of suitable BSN programming support with proper abstraction layers. This paper analyzes the most important requirements for an effective BSN-specific software framework, enabling efficient signal-processing applications. Specifically, we present signal processing in node environment (SPINE), an open-source programming framework, designed to support rapid and flexible prototyping and management of BSN applications. We describe how SPINE efficiently addresses the identified requirements while providing performance analysis on the most common hardware/software sensor platforms. We also report a few high-impact BSN applications that have been entirely implemented using SPINE to demonstrate practical examples of its effectiveness and flexibility. This development experience has notably led to the definition of a SPINE-based design methodology for BSN applications. Finally, lessons learned from the development of such applications and from feedback received by the SPINE community are discussed.

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Citations
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Wearable Sensors for Human Activity Monitoring: A Review

TL;DR: The latest reported systems on activity monitoring of humans based on wearable sensors and issues to be addressed to tackle the challenges are reviewed.
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Multi-sensor fusion in body sensor networks: State-of-the-art and research challenges

TL;DR: This survey discusses clear motivations and advantages of multi-sensor data fusion and particularly focuses on physical activity recognition, aiming at providing a systematic categorization and common comparison framework of the literature, by identifying distinctive properties and parameters affecting data fusion design choices at different levels.
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Survey of Motion Tracking Methods Based on Inertial Sensors: A Focus on Upper Limb Human Motion

TL;DR: Five techniques for motion reconstruction were selected and compared to reconstruct a human arm motion and results show that all but one of the selected models perform similarly (about 35 mm average position estimation error).
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A framework for collaborative computing and multi-sensor data fusion in body sensor networks

TL;DR: C-SPINE, a framework for Collaborative BSNs (CBSNs), is proposed and natively supports multi-sensor data fusion among CBSNs to enable joint data analysis such as filtering, time-dependent data integration and classification.
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Enabling IoT interoperability through opportunistic smartphone-based mobile gateways

TL;DR: This work is proposing a smartphone-based mobile gateway acting as a flexible and transparent interface between different IoT devices and the Internet, which supports opportunistic IoT devices discovery, control and management coupled with data processing, collection and diffusion functionalities.
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